Video deblurring based on bidirectional motion compensation and accurate blur kernel estimation

This paper presents a video deblurring algorithm utilizing the high resolution information of adjacent unblurred frames. First, two motion-compensated predictors of a blurred frame are derived from its neighboring unblurred frames via bidirectional motion compensation. Then, an accurate blur kernel, which is difficult to directly obtain from the blurred frame itself, is computed between the predictors and the blurred frame. Next, a residual deconvolution is employed to reduce the ringing artifacts inherently caused by conventional deconvolution. The blur kernel estimation and deconvolution processes are iteratively performed for the deblurred frame. Experimental results show that the proposed algorithm provides sharper details and smaller artifacts than the state-of-the-art algorithms.